In the current climate of rapid strategic shifts, unexpected global events, and intensified competition, companies are hard-pressed to enhance their adaptability and innovation capabilities. The evolving business landscape demands prompt transformations, and one avenue to achieve that is via the combination of Agile, AI, and advanced analytics, which is also termed Agile AI.
Rooted in software development, Agile serves as a dynamic alternative to conventional, linear waterfall methods. It offers a practical solution when challenges are complex, solutions unknown, requirements unpredictable, work modular, and end-user collaboration essential. This makes Agile ideal for AI and advanced analytics, where ambiguous issues are best tackled through cycles of swift discovery.
Beyond software development, an Agile mindset can redefine how companies perceive value and productivity. Coupled with design thinking and behavioral economics, Agile’s principles of simplicity, face-to-face conversations, regular adjustments, and customer-centric design have found resonance in diverse contexts.
Organizations adopting Agile techniques are uniquely positioned to scale AI benefits: faster market entry, immediate value realization, competitive advantage, swift failure recovery, and enhanced cross-business collaboration. Yet, Agile can be challenging, as it necessitates comprehensive change, which can be costly and time-consuming. As such, many companies find themselves caught between aspiring for agility and truly embodying it.
Three key strategies to unlock AI value with Agile AI
Integrate agile strategy into AI delivery cycle
At its core, deriving benefits from AI relies heavily on an organization’s ability to carry an AI project from conception to execution within production environments. This impacts both customer relations and the final product.
The strategic AI roadmap serves as a comprehensive model that guides organizations to derive and amplify value from their AI initiatives. This model is fundamentally based on an agile, sprint-oriented methodology that focuses on capturing business requirements and iteratively executing AI models through epics and user stories.
However, organizations venturing to exploit agile for obtaining AI value might stumble upon two main pitfalls. The first one is attempting to foster an agile mindset while relying on traditional tools. Companies that err this way might utilize spreadsheets for managing AI projects and email for communication, which can compromise agile values such as “individuals and interactions over processes and tools.”
Contrarily, tools like Microsoft Teams™, Slack™, Trello, and JIRA emphasize collaboration and flexibility. These tools foster transparency within and across project teams, prioritizing continual adjustments to enhance delivery results.
The second pitfall is the hasty investment in agile technologies without analyzing their alignment with business objectives. This oversight might lead to sporadic value pockets. Without top-level support or collaboration among cross-functional teams and business users, advanced analytics and AI teams might adopt agile technologies in isolated silos unequipped for scaling. Therefore, for agile tools to transcend being merely another stack application, they, like any technology in use, require a suitable business case and endorsement from high-level business stakeholders.
Transform corporate culture into a ‘Data Culture’
The triumph of implementing agility to scale AI is not merely reliant on methodology and technology; it’s heavily influenced by the organization’s culture. Many companies, in an effort for a quick solution, hastily hire teams of data scientists with agile experience hoping to create an agile culture. However, they often overlook the need for a strategy to develop a company-wide data culture, one that is receptive to the insights generated by AI.
Similarly, it’s a common misconception that having just one agile team or function is sufficient to scale AI. But if the objective is to adopt AI on a grand scale, a broader data culture is necessary. It’s an ecosystem that everyone must nurture – from the top-down, bottom-up, and even from the outside-in.
Therefore, to truly instill this cultural transformation, leaders should consider engaging with employees across all business areas. Here are a few strategies to consider:
- Make agile certification a mandatory requirement for all new hires to gradually induce a cultural shift.
- Conduct executive boot camps – even remotely, if required – to train the workforce in basic technology and agility from a top-down perspective.
- Provide incentives such as additional holidays or vouchers as rewards for self-certification in new, agile skills like scaled agile or design thinking.
Redefine metrics for success
While agile methodologies and a data-driven culture are undoubtedly crucial for successfully scaling artificial intelligence (AI), they can only bear fruit when complemented by a transformation in how we measure success. This implies rethinking the frequency, methods, and criteria used to evaluate the impact of AI.
To truly leverage the potential of AI and imbibe agility, organizations should transition from rigid planning cycles to continuous, iterative planning. This can only be realized once they have identified and adopted suitable metrics that accurately define success.
Scale rapidly with Agile AI
Survival in today’s business world necessitates adaptability, innovation, and novel ways of working that eliminate obstacles to scaling AI and unveiling new value sources. The focus should shift from technology towards people and processes, thereby placing a premium on agility and ability to scale.
A company should aim to start small, be responsive, and scale rapidly—these are the cornerstones of weathering uncertainties and creating fresh opportunities in the AI, automation, and machine learning arenas. Remember, in the right circumstances, Agile can transform an organization’s ability to scale and extract value from data and AI investments.
